PT  - JOURNAL ARTICLE
AU  - Eric Kolchinsky
TI  - Machine Learning for Structured Finance
AID  - 10.3905/jsf.2018.24.3.007
DP  - 2018 Oct 31
TA  - The Journal of Structured Finance
PG  - 7--25
VI  - 24
IP  - 3
4099  - https://pm-research.com/content/24/3/7.short
4100  - https://pm-research.com/content/24/3/7.full
AB  - Machine learning and artificial intelligence have evolved beyond simple hype and have integrated themselves in business and in popular conversation as an increasing number of smart applications profoundly transform the way we work and live. This article defines machine learning in terms of potential benefits and pitfalls for a nontechnical audience, and gives examples of popular and powerful machine learning algorithms: k-means clustering, principal component analysis, and artificial neural networks. Three important philosophical challenges of machine learning are introduced: the no free lunch theorem, the curse of dimensionality, and the bias–variance trade-off.TOPICS: Big data/machine learning, statistical methods